Academic

A Modular LLM Framework for Explainable Price Outlier Detection

arXiv:2603.20636v1 Announce Type: new Abstract: Detecting product price outliers is important for retail and e-commerce stores as erroneous or unexpectedly high prices adversely affect competitiveness, revenue, and consumer trust. Classical techniques offer simple thresholds while ignoring the rich semantic relationships among product attributes. We propose an agentic Large Language Model (LLM) framework that treats outlier price flagging as a reasoning task grounded in related product detection and comparison. The system processes the prices of target products in three stages: (i) relevance classification selects price-relevant similar products using product descriptions and attributes; (ii) relative utility assessment evaluates the target product against each similar product along price influencing dimensions (e.g., brand, size, features); (iii) reasoning-based decision aggregates these justifications into an explainable price outlier judgment. The framework attains over 75% agreeme

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Shadi Sartipi, John Wu, Sina Ghotbi, Nikhita Vedula, Shervin Malmasi · · 1 min read · 11 views
#cs.CL #cs.CE

arXiv:2603.20636v1 Announce Type: new Abstract: Detecting product price outliers is important for retail and e-commerce stores as erroneous or unexpectedly high prices adversely affect competitiveness, revenue, and consumer trust. Classical techniques offer simple thresholds while ignoring the rich semantic relationships among product attributes. We propose an agentic Large Language Model (LLM) framework that treats outlier price flagging as a reasoning task grounded in related product detection and comparison. The system processes the prices of target products in three stages: (i) relevance classification selects price-relevant similar products using product descriptions and attributes; (ii) relative utility assessment evaluates the target product against each similar product along price influencing dimensions (e.g., brand, size, features); (iii) reasoning-based decision aggregates these justifications into an explainable price outlier judgment. The framework attains over 75% agreement with human auditors on a test dataset, and outperforms zero-shot and retrieval based LLM techniques. Ablation studies show the sensitivity of the method to key hyper-parameters and testify on its flexibility to be applied to cases with different accuracy requirement and auditor agreements.

Executive Summary

This article proposes a novel modular Large Language Model (LLM) framework for explainable price outlier detection in retail and e-commerce settings. The framework treats outlier price flagging as a reasoning task grounded in related product detection and comparison. It processes prices in three stages: relevance classification, relative utility assessment, and reasoning-based decision aggregation. The authors demonstrate the framework's effectiveness, achieving over 75% agreement with human auditors on a test dataset. The study also showcases the framework's flexibility and sensitivity to key hyper-parameters. This pioneering work has significant implications for retail and e-commerce, enabling more accurate and transparent price management, which can enhance competitiveness, revenue, and consumer trust.

Key Points

  • ▸ The framework treats outlier price flagging as a reasoning task grounded in related product detection and comparison.
  • ▸ The framework processes prices in three stages: relevance classification, relative utility assessment, and reasoning-based decision aggregation.
  • ▸ The authors demonstrate the framework's effectiveness, achieving over 75% agreement with human auditors on a test dataset.

Merits

Strength in Explainability

The proposed framework provides explainable price outlier judgments, enabling retailers to understand the reasoning behind price flagging decisions, which can enhance transparency and trust.

Flexibility and Sensitivity

The framework's flexibility and sensitivity to key hyper-parameters demonstrate its adaptability to different scenarios and accuracy requirements, making it a valuable tool for retail and e-commerce applications.

Demerits

Dependence on High-Quality Data

The framework's performance may be sensitive to the quality and relevance of the product attribute data, which can impact the accuracy of the outlier detection results.

Scalability and Computational Complexity

The framework's modular design and three-stage processing may introduce scalability and computational complexity challenges, particularly for large datasets, which can impact its practical usability.

Expert Commentary

This article represents a significant contribution to the field of artificial intelligence and retail technology. The proposed framework's modular design and three-stage processing demonstrate its potential to improve price outlier detection accuracy and transparency. However, its scalability and computational complexity may require further exploration. The framework's ability to provide explainable judgments can inform policy decisions and enhance transparency, which is essential for building trust in retail and e-commerce. As the retail landscape continues to evolve, this framework's implications for price management, supply chain management, and product recommendation systems will become increasingly important.

Recommendations

  • ✓ Future research should investigate the framework's scalability and computational complexity to ensure its practical usability for large datasets.
  • ✓ The framework's ability to provide explainable judgments should be further explored to inform policy decisions and enhance transparency in retail and e-commerce settings.

Sources

Original: arXiv - cs.CL

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